How observational crowdsourcing disrupts serendipity: Designing for pluripotent data with the data design framework
Bibliographic record
Abstract
Serendipity — the process of unexpected discovery and innovation — represents a major opportunity for observational crowdsourcing. We show how observational crowdsourcing platforms may be inadvertently designed to disrupt the serendipity process. We then examine how design decisions affecting crowdsourcing projects and platforms may promote (instead of prevent) serendipity. We introduce the concept of data item pluripotency: the capacity for a data item to hold unexpected uses for a data consumer. We then develop a novel data design framework showing how project and platform design decisions influence the quality dimensions of a project's conceptual model, data items, dataset, and data use. The data design framework presents a powerful way to understand the relationship between design decisions, the different components of a crowdsourcing project, and the lower- and higher-order data quality dimensions we aim to cultivate. We conclude by discussing the implications for observational crowdsourcing and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".